PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F18%3A39913349" target="_blank" >RIV/00216275:25410/18:39913349 - isvavai.cz</a>
Result on the web
<a href="http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703" target="_blank" >http://www.amfiteatrueconomic.ro/ArticolEN.aspx?CodArticol=2703</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.24818/EA/2018/47/185" target="_blank" >10.24818/EA/2018/47/185</a>
Alternative languages
Result language
angličtina
Original language name
PREDICTING ABNORMAL STOCK RETURN VOLATILITY USING TEXTUAL ANALYSIS OF NEWS - A META-LEARNING APPROACH
Original language description
Textual analysis of news articles is increasingly important in predicting stock prices. Previous research has intensively utilized the textual analysis of news and other firm-related documents in volatility prediction models. It has been demonstrated that the news may be related to abnormal stock price behavior subsequent to their dissemination. However, previous studies to date have tended to focus on linear regression methods in predicting volatility. Here, we show that non-linear models can be effectively employed to explain the residual variance of the stock price. Moreover, we use meta-learning approach to simulate the decision-making process of various investors. The results suggest that this approach significantly improves the prediction accuracy of abnormal stock return volatility. The fact that the length of news articles is more important than news sentiment in predicting stock return volatility is another important finding. Notably, we show that Rotation forest performs particularly well in terms of both the accuracy of abnormal stock return volatility and the performance on imbalanced volatility data.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
50204 - Business and management
Result continuities
Project
<a href="/en/project/GA16-19590S" target="_blank" >GA16-19590S: Topic and sentiment analysis of multiple textual sources for corporate financial decision-making</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Amfiteatru Economic
ISSN
1582-9146
e-ISSN
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Volume of the periodical
20
Issue of the periodical within the volume
47
Country of publishing house
RO - ROMANIA
Number of pages
17
Pages from-to
185-201
UT code for WoS article
000427829800012
EID of the result in the Scopus database
2-s2.0-85041616998